By cryptoadmin 
May 23, 2025
The GIST Large language model accurately predicts online chat derailments 
Gaby Clark
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Robert Egan
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Online chat rooms and social networking platforms frequently experience harmful behavior as discussions drift from their intended topics toward personal conflict. Traditional predictive models typically depend on platform-specific data, limiting their applicability and increasing implementation costs.
In a new study, researchers at the University of Tsukuba applied a zero-shot prediction method to LLMs to detect conversational derailments. The performance of various untrained LLMs was compared to that of a deep learning model trained on curated datasets. The results showed that untrained LLMs achieved comparable, and in some cases superior, accuracy.
These findings, published in the journal IEEE Access, suggest that platform operators can implement effective moderation tools at reduced cost by leveraging general-purpose LLMs, supporting healthier online communities across diverse platforms.
More information: Kenya Nonaka et al, Zero-Shot Prediction of Conversational Derailment With Large Language Models, IEEE Access (2025). DOI: 10.1109/ACCESS.2025.3554548
Journal information: IEEE Access Provided by University of Tsukuba Citation: Large language model accurately predicts online chat derailments (2025, May 23) retrieved 23 May 2025 from https://techxplore.com/news/2025-05-large-language-accurately-online-chat.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
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